Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models Article Swipe
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.14353
Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high variability across patients. Deep learning has improved segmentation accuracy and efficiency, but it still relies heavily on expert annotations and struggles with the complexities of medical images. The small size of medical image datasets and the high cost of data acquisition further limit the performance of segmentation networks. Diffusion models, with their iterative denoising process, offer a promising alternative for better detail capture in segmentation. However, they face difficulties in accurately segmenting small targets and maintaining the precision of boundary details. This article discusses the importance of medical image segmentation, the limitations of current deep learning approaches, and the potential of diffusion models to address these challenges.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.14353
- https://arxiv.org/pdf/2411.14353
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404649493
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404649493Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2411.14353Digital Object Identifier
- Title
-
Enhancing Medical Image Segmentation with Deep Learning and Diffusion ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-21Full publication date if available
- Authors
-
Houze Liu, Tong Zhou, Yun Xiang, Aoran Shen, Jiacheng Hu, Junliang DuList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.14353Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.14353Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2411.14353Direct OA link when available
- Concepts
-
Artificial intelligence, Diffusion, Image (mathematics), Segmentation, Deep learning, Computer science, Image segmentation, Computer vision, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4404649493 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2411.14353 |
| ids.doi | https://doi.org/10.48550/arxiv.2411.14353 |
| ids.openalex | https://openalex.org/W4404649493 |
| fwci | |
| type | preprint |
| title | Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12422 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9196000099182129 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Radiomics and Machine Learning in Medical Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.5940267443656921 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C69357855 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5601050853729248 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q163214 |
| concepts[1].display_name | Diffusion |
| concepts[2].id | https://openalex.org/C115961682 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5587486028671265 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[2].display_name | Image (mathematics) |
| concepts[3].id | https://openalex.org/C89600930 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5331050157546997 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[3].display_name | Segmentation |
| concepts[4].id | https://openalex.org/C108583219 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5049863457679749 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[4].display_name | Deep learning |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.4921228289604187 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C124504099 |
| concepts[6].level | 3 |
| concepts[6].score | 0.48705819249153137 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[6].display_name | Image segmentation |
| concepts[7].id | https://openalex.org/C31972630 |
| concepts[7].level | 1 |
| concepts[7].score | 0.41621676087379456 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[7].display_name | Computer vision |
| concepts[8].id | https://openalex.org/C121332964 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0707867443561554 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[8].display_name | Physics |
| concepts[9].id | https://openalex.org/C97355855 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[9].display_name | Thermodynamics |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.5940267443656921 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/diffusion |
| keywords[1].score | 0.5601050853729248 |
| keywords[1].display_name | Diffusion |
| keywords[2].id | https://openalex.org/keywords/image |
| keywords[2].score | 0.5587486028671265 |
| keywords[2].display_name | Image (mathematics) |
| keywords[3].id | https://openalex.org/keywords/segmentation |
| keywords[3].score | 0.5331050157546997 |
| keywords[3].display_name | Segmentation |
| keywords[4].id | https://openalex.org/keywords/deep-learning |
| keywords[4].score | 0.5049863457679749 |
| keywords[4].display_name | Deep learning |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.4921228289604187 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/image-segmentation |
| keywords[6].score | 0.48705819249153137 |
| keywords[6].display_name | Image segmentation |
| keywords[7].id | https://openalex.org/keywords/computer-vision |
| keywords[7].score | 0.41621676087379456 |
| keywords[7].display_name | Computer vision |
| keywords[8].id | https://openalex.org/keywords/physics |
| keywords[8].score | 0.0707867443561554 |
| keywords[8].display_name | Physics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2411.14353 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2411.14353 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2411.14353 |
| locations[1].id | doi:10.48550/arxiv.2411.14353 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2411.14353 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5104250303 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Houze Liu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Liu, Houze |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5091723887 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Tong Zhou |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhou, Tong |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5007650182 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2982-425X |
| authorships[2].author.display_name | Yun Xiang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xiang, Yanlin |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5058804601 |
| authorships[3].author.orcid | https://orcid.org/0009-0009-7429-1254 |
| authorships[3].author.display_name | Aoran Shen |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Shen, Aoran |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5107194762 |
| authorships[4].author.orcid | https://orcid.org/0009-0003-6588-2868 |
| authorships[4].author.display_name | Jiacheng Hu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hu, Jiacheng |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5010332378 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2794-2327 |
| authorships[5].author.display_name | Junliang Du |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Du, Junliang |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2411.14353 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12422 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9196000099182129 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Radiomics and Machine Learning in Medical Imaging |
| related_works | https://openalex.org/W4375867731, https://openalex.org/W2611989081, https://openalex.org/W2731899572, https://openalex.org/W4230611425, https://openalex.org/W4294635752, https://openalex.org/W4304166257, https://openalex.org/W4383066092, https://openalex.org/W3215138031, https://openalex.org/W4379231730, https://openalex.org/W1522196789 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2411.14353 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2411.14353 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2411.14353 |
| primary_location.id | pmh:oai:arXiv.org:2411.14353 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2411.14353 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2411.14353 |
| publication_date | 2024-11-21 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 82 |
| abstract_inverted_index.as | 14 |
| abstract_inverted_index.in | 89, 95 |
| abstract_inverted_index.is | 3 |
| abstract_inverted_index.it | 10, 38 |
| abstract_inverted_index.of | 50, 56, 64, 71, 104, 112, 118, 126 |
| abstract_inverted_index.on | 42 |
| abstract_inverted_index.to | 129 |
| abstract_inverted_index.The | 53 |
| abstract_inverted_index.and | 19, 24, 35, 45, 60, 100, 123 |
| abstract_inverted_index.but | 37 |
| abstract_inverted_index.for | 5, 85 |
| abstract_inverted_index.has | 31 |
| abstract_inverted_index.low | 15 |
| abstract_inverted_index.the | 48, 61, 69, 102, 110, 116, 124 |
| abstract_inverted_index.yet | 9 |
| abstract_inverted_index.Deep | 29 |
| abstract_inverted_index.This | 107 |
| abstract_inverted_index.cost | 63 |
| abstract_inverted_index.data | 65 |
| abstract_inverted_index.deep | 120 |
| abstract_inverted_index.face | 93 |
| abstract_inverted_index.high | 25, 62 |
| abstract_inverted_index.size | 55 |
| abstract_inverted_index.such | 13 |
| abstract_inverted_index.they | 92 |
| abstract_inverted_index.with | 47, 76 |
| abstract_inverted_index.faces | 11 |
| abstract_inverted_index.image | 1, 58, 114 |
| abstract_inverted_index.limit | 68 |
| abstract_inverted_index.offer | 81 |
| abstract_inverted_index.small | 54, 98 |
| abstract_inverted_index.still | 39 |
| abstract_inverted_index.their | 77 |
| abstract_inverted_index.these | 131 |
| abstract_inverted_index.across | 27 |
| abstract_inverted_index.better | 86 |
| abstract_inverted_index.detail | 87 |
| abstract_inverted_index.expert | 43 |
| abstract_inverted_index.models | 128 |
| abstract_inverted_index.normal | 20 |
| abstract_inverted_index.relies | 40 |
| abstract_inverted_index.Medical | 0 |
| abstract_inverted_index.address | 130 |
| abstract_inverted_index.article | 108 |
| abstract_inverted_index.between | 17 |
| abstract_inverted_index.capture | 88 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.current | 119 |
| abstract_inverted_index.further | 67 |
| abstract_inverted_index.heavily | 41 |
| abstract_inverted_index.images. | 52 |
| abstract_inverted_index.lesions | 18 |
| abstract_inverted_index.medical | 51, 57, 113 |
| abstract_inverted_index.models, | 75 |
| abstract_inverted_index.targets | 99 |
| abstract_inverted_index.unclear | 22 |
| abstract_inverted_index.However, | 91 |
| abstract_inverted_index.accuracy | 34 |
| abstract_inverted_index.accurate | 6 |
| abstract_inverted_index.boundary | 105 |
| abstract_inverted_index.clinical | 7 |
| abstract_inverted_index.contrast | 16 |
| abstract_inverted_index.datasets | 59 |
| abstract_inverted_index.details. | 106 |
| abstract_inverted_index.improved | 32 |
| abstract_inverted_index.learning | 30, 121 |
| abstract_inverted_index.process, | 80 |
| abstract_inverted_index.tissues, | 21 |
| abstract_inverted_index.Diffusion | 74 |
| abstract_inverted_index.denoising | 79 |
| abstract_inverted_index.diffusion | 127 |
| abstract_inverted_index.discusses | 109 |
| abstract_inverted_index.iterative | 78 |
| abstract_inverted_index.networks. | 73 |
| abstract_inverted_index.patients. | 28 |
| abstract_inverted_index.potential | 125 |
| abstract_inverted_index.precision | 103 |
| abstract_inverted_index.promising | 83 |
| abstract_inverted_index.struggles | 46 |
| abstract_inverted_index.accurately | 96 |
| abstract_inverted_index.challenges | 12 |
| abstract_inverted_index.diagnoses, | 8 |
| abstract_inverted_index.importance | 111 |
| abstract_inverted_index.segmenting | 97 |
| abstract_inverted_index.acquisition | 66 |
| abstract_inverted_index.alternative | 84 |
| abstract_inverted_index.annotations | 44 |
| abstract_inverted_index.approaches, | 122 |
| abstract_inverted_index.boundaries, | 23 |
| abstract_inverted_index.challenges. | 132 |
| abstract_inverted_index.efficiency, | 36 |
| abstract_inverted_index.limitations | 117 |
| abstract_inverted_index.maintaining | 101 |
| abstract_inverted_index.performance | 70 |
| abstract_inverted_index.variability | 26 |
| abstract_inverted_index.complexities | 49 |
| abstract_inverted_index.difficulties | 94 |
| abstract_inverted_index.segmentation | 2, 33, 72 |
| abstract_inverted_index.segmentation, | 115 |
| abstract_inverted_index.segmentation. | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |